Evaluation of Interpolation Techniques for Enhanced Super Resolution in Bio-Medical Imaging
Abstract- This paper evaluates the performance of a unique image interpolation scheme that fuses bicubic interpolation with two dimensional interpolation filter versus the conventional interpolation methods. A high resolution image is first obtained by performing bicubic interpolation on the input image. The super resolution image is then obtained by implementing column and row interpolation filtering on the bicubic interpolated image. The 2D interpolation filter caters excessive edge softness and filters the interpolated pixel to generate perceptually superior quality image. The proposed interpolation method produces the least mean square error as compare to the linear interpolation methods. The results obtained shows augmented quality and Peak Signal to Noise Ratio (PSNR) of the interpolated images with our proposed scheme over linear interpolation methods.
Keywords- Image Interpolation; Two dimensional filter; Linear; Super Resolution
Doctors incessantly seek ways to acquire superior high resolution digital X-ray images for diagnosis, by means of expensive and unremittingly advance medical imaging systems. Thus it becomes imperative to ascertain economical, precise, proficient and powerful methods to acquire high resolution images by means of the present medical imaging equipment. Super resolution (SR) (or HR) image reconstruction is one of the most effervescent research area that employ signal processing techniques to acquire HR image from single or multiple LR images -. The process of producing a high-resolution image (HR) from single or multiple low-resolution images (LR) is known as Super resolution. It can be obtained by using Transform based methods or Interpolation based methods. Image interpolation methods estimate unknown pixels of a high-resolution image from the known pixels of a low-resolution image to produce a high-resolution image.
Biomedical imaging, satellite imaging, surveillance, video processing, scanning, printing etc are some of the major applications of Super resolution. In biomedical imaging the enlargement of low resolution digital X-ray images of patients by using super resolution algorithms is a paradigm of relevance of super resolution. It is much easier for the doctors, clinicians and researchers to perform diagnosis by using enhanced super resolution X-ray images. Medical X-ray images of femur, knee, skull, tibia, etc of patients is frequently required to analyze morphology and amount of fracture. It also shows anatomical features of hard tissue and assists premature diagnosis of bone ossification and tumor escalation. Several techniques have been proposed to surmise super resolution image from a low resolution image but either they lack accuracy or computational efficiency. Zigzag errors and the blurring effects are the most common effects observed in the super resolution image expansion. Sharpness and liberty from artifacts in edges are two crucial features in the perceived quality of images. The portion of an image where luminance is not consistent is known as Edge. Diagonal to the edge direction and beside the edge direction luminance fluctuates abruptly and steadily correspondingly. Thus along edges and across edges pixel values have good correlation and poor correlation respectively .
II. Related Work
In , ,  edge-directed interpolation (EDI) methods use the local statistical and geometrical properties to interpolate the unknown pixel values. The NEDI method  is a hybrid approach that switches between bilinear and covariance-based interpolation. In  Improved New Edge directed Interpolation (iNEDI) technique alters the NEDI method by differing the width of the training window relating to the edge size and uses a circular window instead of rectangular window to obtain better PSNR performance. In  Iterative Curvature Based Interpolation (ICBI) technique takes into account the results of the isophote contour, curvature enhancement and curvature continuity. Despite the impressive performance, the covariance-dependent adaptation is prohibitive due to its complexity. Conventional linear interpolation techniques such as bilinear and bicubic etc fail to confine the swift evolving data around edges and consequently create interpolated images with noise and indistinct edges. Non-linear interpolation is favored for performance while linear for computational ease . Edge directed interpolation scheme have been used by researchers to produce enhanced image quality . The bicubic interpolation merged with two-dimensional (2D) unique interpolation filtering in  produced visually pleasant and precise super resolution images. This paper evaluates the performance of the proposed algorithm by comparing PSNR and MSE with the conventional linear interpolation methods. The proposed scheme was applied to medical X-ray images acquired from GE Healthcare. The results show improvement in PSNR and MSE performance of medical X-ray images based on our scheme. A complete set of statistical values were obtained to analyze the results thoroughly. The standard deviation, mean, mode and range reveal the consistency of the proposed interpolation scheme over the bilinear, bicubic and nearest neighbor interpolation techniques.
The most vital detail in an image is edge, thus to maintain a sharp image, loss or smoothness of edges should be avoided. The linear interpolations techniques are well known for their efficiency over non linear interpolation techniques but lack the quality of performance produced by the nonlinear interpolation techniques. We propose an interpolation technique to surmount the reduced performance of linear interpolation technique. It uses Bilinear [1 1]/2 or the 6-tap filter [1 -5 20 20 -5 1] /32 filter used in the H.264 coding standard to preserve edges. In our work 6-tap filter [1 -5 20 20 -5 1] /32 in equation (1) was used due to superior performance and high pass distinctiveness to obtain clear medical X-ray images . The properties of interpolation filter design have been explained .
As shown in Fig. 1, Fig. 2 and Fig. 3 the proposed interpolation system works in the following three stages. In the first stage, a high resolution image is obtained by performing bicubic interpolation on a low resolution image. To produce a filtered image, it is then passed through a 6-tap filter with coefficients [1 -5 20 20 -5 1]. In the second stage, column interpolation is executed by merging bicubic interpolated image and the filtered image obtained from the first phase. To obtain the final filtered image this column interpolated image is passed through the 6-tap filter mentioned above. In the third stage, row interpolation is implemented to produce super resolution image by merging column interpolated image and final filtered image obtained from the second phase. For an RGB image the entire process is applied to all the layers respectively to produce the final super resolution image. The selection of the 6-tap filter with coefficients [1 -5 20 20 -5 1] is made due to its more high pass and less low pass performance which preserves edges in an image . To protect and distill the edges and produce superior quality images bicubic interpolation is fused with 2D interpolation filter. This unique fusion of interpolation methods yields remarkable results. MatLab's filter2 command performs 2D filtering of image by using 2D correlation. The result is the central component of the correlation that is of equivalent in width as the input image. The 2D convolution with the filter coefficient rotated 180 degrees is implemented in place of 2D correlation . Thus the filter2 rotates the 6-tap filter with coefficients [1 -5 20 20 -5 1] 180 degrees to create a convolution kernel, it subsequently calls conv2, the 2D convolution function of MatLab, to execute the filtering operation. By default, filter2 then picks the central section of the convolution that is the same dimension as the input image, and returns this as the answer . The effectiveness and accomplishment of the proposed scheme is shown by the improved PSNR performance and visual quality of digital medical X-ray images.
IV. Experimental Results and Discussions
The set of seven digital X-ray RGB images shown in figure 4 taken from GE healthcare were used to evaluate the performance of our method with bilinear, bicubic, and nearest neighbor interpolation methods . The seven digital X-ray RGB images were enlarged with an enlargement factor of 4X by using bilinear, bicubic, and nearest neighbor by Trung Duong  and MatLab's built in command "imresize" and proposed interpolation scheme. The 4X enlarged digital X-ray RGB images of skull is shown in figure 5 for visual comparison. The Peak signal to Noise Ratio (PSNR) of all the seven digital X-ray RGB images is shown in figure 6. The Peak signal to Noise Ratio (PSNR) of all the seven digital X-ray RGB images was calculated by using equation (2) .
The results demonstrate sharpness in the perceived quality of images and the efficacy of the proposed technique. We can observe that annoying ringing artifacts are dramatically suppressed in the interpolated images by our scheme due to 2D interpolation filtering. It can be distinguished that our method produces the image with the utmost visual quality. To show the improved performance of our scheme PSNR (dB) values of 4X enlarged digital medical X-ray RGB images have been plotted in MatLab Fig. 6. The plot reveals the marked increase in PSNR with the proposed scheme versus the conventional linear interpolation methods. The curve of proposed method is clearly higher than the bilinear, bicubic and nearest neighbor interpolation methods. Peak signal to noise ratios (dB) and Mean Square Error (MSE) achieved by evaluating images enlarged by approximately 4X factor with reference images is summed up in table I and table II respectively. The new proposed technique yielded the superlative results with an average PSNR of 44.32017 dB for 4X enlarged RGB images. The presented interpolation method with 4X magnification factor yielded an average MSE of 2.881244 dB for 4X enlarged RGB images. The results for bilinear, bicubic and nearest neighbor interpolation techniques have been achieved with a Matlab implementation of the proposed algorithms. Thus our interpolation scheme is undoubtedly advanced to linear interpolation techniques. Statistical analysis of PSNR and MSE of 4X enlarged medical images has been shown in figure 7 and figure 8 respectively. The minimum value, maximum value, average (mean), median, mode, standard deviation (std) and range of proposed interpolation scheme has been compared with bilinear, bicubic and nearest neighbor interpolation techniques. Standard deviation is a measure of how widely values are dispersed from the average value (the mean). The lowest standard deviation value of PSNR and MSE with the proposed scheme shows the consistency of our scheme as compare to bilinear, bicubic and nearest neighbor interpolation techniques.
This paper extensively evaluates the performance of the presented interpolation technique versus the conventional linear interpolation techniques for accurate single image interpolation of digital medical X-rays images. The existing problems of accuracy, artifact, blur etc by linear interpolation techniques have been catered by fusing bicubic interpolation with 2D interpolation 6-tap filter. The improved performance of the proposed technique has been verified by widespread imitation and evaluation with bilinear, bicubic and nearest neighbor interpolation methods. The consistent PSNR and MSE performance with exceptional perceptual quality images shows the tremendous achievement of the proposed scheme.
Thanks to Andrea Giachetti and Nicola Asuni for providing ICBI and iNEDI implementation. Thanks to Patrick Vandewalle and Athanasopoulos Dionysios for their support.
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